Distribution of Synthetic Populations of Japan for Social Scientists and Social Simulation Researchers

Murata T., Harada T., Wa M.I., et al. Distribution of Synthetic Populations of Japan for Social Scientists and Social Simulation Researchers. Proceedings - International Conference on Machine Learning and Cybernetics 2019-July (2019); https://doi.org/10.1109/ICMLC48188.2019.8949245.In this paper, we describe how synthesized populations are essential in real-scale social simulations (RSSS), and the current situation of the population synthesis for whole populations in Japan. RSSS is simulations using the real number of populations or households in social simulations. This paper describes how we have completed to synthesize multiple sets of populations based on the statistics of each local government in Japanese national census in 2000,2005,2010 and 2015. We have started to distribute those multiple sets of the synthesized populations for researchers of RSSSs in Japan. In distributing the synthesized populations, we should set some regulations in order to protect personal or private information in the synthesized populations

Distribution system for japanese synthetic population data with protection level

Murata T., Date S., Goto Y., et al. Distribution system for japanese synthetic population data with protection level. Proceedings - International Conference on Machine Learning and Cybernetics 2020-December, 187 (2020); https://doi.org/10.1109/ICMLC51923.2020.9469550.In this paper, we introduce a distribution system of synthesized data of Japanese population using Interdisciplinary Large-scale Information Infra-structures in Japan. Synthetic population is synthesized based on the statistics of the census that are conducted by the government and publicly released. Therefore, the synthesized data have no privacy data. However, it is easy to estimate the compositions of households, working status in a certain area from the synthetic population. Therefore, we currently distribute the synthesized data only for public or academic purposes. For academic purposes, it is important to encourage scholars or researchers to use a large-scale data of households, we define protection levels for the attributes in the synthetic populations. According to the protection levels, we distribute the data with proper attributes to those who try to use them. We encourage researchers to use the synthetic populations to be familiar to large-scale data processing

Linkage Identification by Non-monotonicity Detection for Overlapping Functions

This paper presents the linkage identification by non-monotonicity detection (LIMD) procedure and its extension for overlapping functions by introducing the tightness detection (TD) procedure. The LIMD identifies linkage groups directly by performing order-2 simultaneous perturbations on a pair of loci to detect monotonicity/non-monotonicity of fitness changes. The LIMD can identify linkage groups with at most order of k when it is applied to O(2k) strings. The TD procedure calculates tightness of linkage between a pair of loci based on the linkage groups obtained by the LIMD. By removing loci with weak tightness from linkage groups, correct linkage groups are obtained for overlapping functions, which were considered difficult for linkage identification procedures

An automated ligand evolution system using Bayesian optimization algorithm

Ligand docking checks whether a drug chemical called ligand matches the target receptor protein of human organ or not. Docking by computer simulation is becoming popular in drug design process to reduce cost and time of the chemical experiments. This paper presents a novel approach generating optimal ligand structures from scratch based on de novo ligand design approach employing Bayesian optimization algorithm to realize an automated design of drug and other chemical structures. The proposed approach searches an optimal structure of ligand that minimizes bond energy to the receptor protein, and the structure of ligand is generated by adding small fragments of molecules to the base structure. The decision of adding fragments are controlled by Bayesian optimization algorithm which is considered as a promising approach in probabilistic model-building genetic algorithms. We have built a system that automatically generates an optimal structure of ligand, and through numerical experiments performed on a PC cluster, we show the effectiveness of our approach compared to the conventional approach using classical genetic algorithms